Cross sectional imaging is an imaging technique which produces a large series of two-dimensional (2D) images of a subject, e.g., a human subject. Examples of cross sectional imaging techniques include computerized tomography (CT), magnetic resonance imaging (MRI), positron emission tomography (PET), SPECT scanning, ultrasonography (US), among others. A set of cross sectional images for a single patient, e.g., for different axially located cross-sections or for the same cross section at different times can be considered three dimensional (3D) image data, and even four dimensional (4D) image data for combinations of axial and temporal cross sectional images.
Various analytical approaches can be applied to the cross sectional images to detect and highlight portions of the patient's anatomy of interest. For example, the cross sectional images can be processed by segmentation, which generally involves separating objects not of interest from objects of interest, e.g., extracting anatomical surfaces, structures, or regions of interest from the images for the purposes of anatomical identification, diagnosis, evaluation, and volumetric measurements. In detecting tumor changes with therapies, volumetric measurement can be more accurate and sensitive than conventional linear measurements. 3D segmentation of cross sectional images provides a feasible way to quantify tumor volume and volume changes over time.
However, segmentation of primary and metastatic tumors, or certain organs (e.g., liver, spleen and kidney), which can be highly heterogeneous, is challenging. There is a need for accurate and efficient delineation of these objects and measurement of their volumes, e.g., for better therapy response assessment, monitor organ regeneration after transplantation, and to make non-invasive diagnoses in both clinical trials and clinical practice.